Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support

Although the future mean of intracranial pressure (ICP) is of critical concern of many clinicians for timely medical treatment, the problem of forecasting the future ICP mean has not been addressed yet. In this paper, we present a nonlinear autoregressive with exogenous input artificial neural network based mean forecast algorithm (ANN_NARX-MFA) to predict the ICP mean of the future windows based on features extracted from past windows and segmented sub-windows. We compare its performance with nonlinear autoregressive artificial neural network algorithm (ANN_NAR) without features extracted from window segmentation. Experimental results showed that, ANN_NARX-MFA algorithm outperforms ANN_NAR algorithm in prediction accuracy, because additional features extracted from finer segmented sub-windows help to catch the subtle changes of ICP trends. This verifies the effectiveness of decomposing the whole window into sub-windows to obtain features in making predictions on future windows. Based on the forecast of ICP mean, medical treatments can be planned in advance to control ICP elevation, in order to maximize recovery and optimize outcome.